Supplementary material to the Paper and Presentation B4.4 Machine Learning Based Procedural Circuit Sizing and DC Operating Point Prediction at SMACD 2021.
Note: For training models precept has to be installed manually.
For everything else simply run
$ pip install -r ./requirements.txt
in this repository.
Make sure Jupyter Lab is installed, then navigate into
the notebooks folder and start jupyter.
$ cd notebooks
$ jupyter labThe data for training the models is obtained by characterizing 130nm, 90nm and 45nm PTM devices, as seen in pyrdict.
For convenience, you can run
$ ./ptm-setup.shwhich will create a folder called ./lib containing these 3 libraries.
Sizing a circuit requires machine learning models trained for mapping
electrical characteristics of Primitive Devices to corresponding geometric
values. For this example, trained models are given in the models directory of
this repository. These models were trained (as shown in
notebooks/model_training.ipynb) with the
precept library on the
data generated in the previous section.
See notebooks/sym_sizing.ipynb.
See notebooks/moa_sizing.ipynb.
@inproceedings{ edlab2021b44
, author={Y. {Uhlmann} and M. {Essich} and M. {Schweikardt} and J. {Scheible} and C. {Curio}}
, title={Machine Learning Based Procedural Circuit Sizing and DC Operating Point Prediction}
, booktitle={2021 17th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)}
, year={2021}
, volume={17},
, pages={}
, }
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